{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "family\n",
       "jSMSHider            767\n",
       "Zitmo                665\n",
       "Lovetrap             650\n",
       "GingerMaster         600\n",
       "GoldDream            543\n",
       "BaseBridge           479\n",
       "Jifake               450\n",
       "Plankton             450\n",
       "KMin                 400\n",
       "ADRD                 377\n",
       "Geinimi              305\n",
       "HippoSMS             293\n",
       "AnserverBot          275\n",
       "DroidDreamLight      226\n",
       "Pjapps               214\n",
       "Zsone                206\n",
       "BgServ               122\n",
       "SMSReplicator        112\n",
       "YZHC                  83\n",
       "DroidKungFu3          60\n",
       "Asroot                55\n",
       "FakeNetflix           39\n",
       "DroidDeluxe           35\n",
       "RogueSPPush           34\n",
       "DroidCoupon           33\n",
       "Spitmo                27\n",
       "BeanBot               25\n",
       "DroidKungFu4          22\n",
       "Endofday              17\n",
       "GGTracker             16\n",
       "Gone60                14\n",
       "Walkinwat             11\n",
       "Crusewin               9\n",
       "zHash                  9\n",
       "RogueLemon             8\n",
       "DroidKungFuSapp        6\n",
       "DogWars                5\n",
       "CoinPirate             4\n",
       "GamblerSMS             4\n",
       "DroidKungFuUpdate      3\n",
       "NickyBot               3\n",
       "GPSSMSSpy              2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame.from_csv(\"malgenome_new_dataset.csv\")\n",
    "df.groupby('family').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
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 },
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}
